Expanded information retrieval using full-text searching

Abstract

The value of full text for expanding information retrieval was examined. Two full-text databases were used: Textpresso for neuroscience and ScienceDirect. Queries representing different categories were used to search different text fields (titles, abstracts, full text and, where possible, keywords). Searching the full-text field relative to the commonly used abstracts field increases retrievals by one or more orders of magnitude, depending on the categories selected. For phenomena-type categories (e.g. blood flow, thermodynamic equilibrium, etc.), retrievals are enhanced by about an order of magnitude. For infrastructure-type categories (e.g. equipment types, sponsors, suppliers, databases, etc.), retrievals are enhanced by well over an order of magnitude, and sometimes multiple orders of magnitude. Use of combination terms along with proximity specification capability is a very powerful feature for retrieving relevant records from full-text searching, and can be useful for applications like literature-related discovery.